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Grant: Collaborative Research and Development Grants

York University

$229,500.00

3D Mobile Mapping Using Artificial Intelligence

EF 2022-23

Résumé
Critical infrastructure, the interdependent networks of utilities, transportation, and facilities, are the backbone of Canada's economy and society. Although Canada is the 2nd largest country in the world, with the world's 10th largest economy, one-third of its infrastructure is in need of a significant update. Regular, rigorous monitoring is necessary to ensure the safe and efficient operation of the infrastructure. However, current human-centric inspection practices are inadequate and laborious to fulfill this need. Inexpensive and more efficient technologies that are capable of continuous and accurate monitoring are necessary. 3D mobile mapping system (MMS) is an emerging solution that can collect visual sensory data to survey entire infrastructure with high speed using a common moving platform. There have been major advancements in acquiring sensory data to produce 3D point clouds; however, the post-acquisition processing of these datasets remains a challenge to successfully generate 3D high-fidelity maps, which are semantically interpretable, geometrically detailed and georeferenced with survey grade accuracy. Teledyne Optech, headquartered in Toronto, is a world leader in the MMS field. In collaboration with Teledyne Optech, this NSERC CRD project will develop an advanced data processing system using a specific type of artificial intelligence (AI), called deep neural network, which has recently achieved remarkable success in computer and robotic vision and machine learning. This work will allow for the autonomous recognition of infrastructure assets using the MMS data and high-quality 3D models of critical networks, thus contributing to the field of infrastructure management and improving urban sustainability as a whole. The technologies developed will be able to compete with human-centric technique and reduce the time required for post-acquisition data processing; they will facilitate the operation of the MMS in GPS-denied environments and allow users to adjust survey parameters as needed in real time. The HQP trained through this program will contribute to various Canadian industries and the fields of AI technologies, infrastructure management, urban planning, and MMS.
Programme
Collaborative Research and Development Grants
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data

Tel que publié

Application id
537080-2018
Name nom
Sohn, Gunho G
Department département
Earth and Space Science and Engineering
Organization id
38
Institution établissement
York University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2022
Competition year année de concours
2018
Award amount
229500
Program id
CRDPJ
Program name en
Collaborative Research and Development Grants
Program name fr
Subventions de recherche et développement coopérative
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
604
Area of application group en
Construction, urban and rural planning
Area of application group fr
Construction, urbanisme et aménagement rural
Area of application en
Surveying and photogrammetry
Area of application fr
Arpentage et photogrammétrie
Research subject code
1004
Research subject group en
Civil engineering
Research subject group fr
Génie civil
Research subject en
Survey engineering and remote sensing
Research subject fr
Génie de l'arpentage et télédétection
Installment
inst:4
Partie
1
Nb partie
4
Application title
3D Mobile Mapping Using Artificial Intelligence
Application summary
Critical infrastructure, the interdependent networks of utilities, transportation, and facilities, are the backbone of Canada's economy and society. Although Canada is the 2nd largest country in the world, with the world's 10th largest economy, one-third of its infrastructure is in need of a significant update. Regular, rigorous monitoring is necessary to ensure the safe and efficient operation of the infrastructure. However, current human-centric inspection practices are inadequate and laborious to fulfill this need. Inexpensive and more efficient technologies that are capable of continuous and accurate monitoring are necessary. 3D mobile mapping system (MMS) is an emerging solution that can collect visual sensory data to survey entire infrastructure with high speed using a common moving platform. There have been major advancements in acquiring sensory data to produce 3D point clouds; however, the post-acquisition processing of these datasets remains a challenge to successfully generate 3D high-fidelity maps, which are semantically interpretable, geometrically detailed and georeferenced with survey grade accuracy. Teledyne Optech, headquartered in Toronto, is a world leader in the MMS field. In collaboration with Teledyne Optech, this NSERC CRD project will develop an advanced data processing system using a specific type of artificial intelligence (AI), called deep neural network, which has recently achieved remarkable success in computer and robotic vision and machine learning. This work will allow for the autonomous recognition of infrastructure assets using the MMS data and high-quality 3D models of critical networks, thus contributing to the field of infrastructure management and improving urban sustainability as a whole. The technologies developed will be able to compete with human-centric technique and reduce the time required for post-acquisition data processing; they will facilitate the operation of the MMS in GPS-denied environments and allow users to adjust survey parameters as needed in real time. The HQP trained through this program will contribute to various Canadian industries and the fields of AI technologies, infrastructure management, urban planning, and MMS.